Overview

Dataset statistics

Number of variables21
Number of observations23906
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory156.0 B

Variable types

Text3
DateTime1
Categorical10
Numeric7

Alerts

Amount_paid_for_insurance is highly overall correlated with Price_($)High correlation
Dealer_Name is highly overall correlated with Dealer_NoHigh correlation
Dealer_No is highly overall correlated with Dealer_NameHigh correlation
Engine is highly overall correlated with TransmissionHigh correlation
Price_($) is highly overall correlated with Amount_paid_for_insuranceHigh correlation
Transmission is highly overall correlated with EngineHigh correlation
Car_id has unique valuesUnique
Claim_amount has 21516 (90.0%) zerosZeros

Reproduction

Analysis started2024-05-30 10:59:32.393720
Analysis finished2024-05-30 11:00:20.716925
Duration48.32 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Car_id
Text

UNIQUE 

Distinct23906
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:21.037866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters286872
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23906 ?
Unique (%)100.0%

Sample

1st rowC_CND_000001
2nd rowC_CND_000002
3rd rowC_CND_000003
4th rowC_CND_000004
5th rowC_CND_000005
ValueCountFrequency (%)
c_cnd_000001 1
 
< 0.1%
c_cnd_000014 1
 
< 0.1%
c_cnd_000005 1
 
< 0.1%
c_cnd_000006 1
 
< 0.1%
c_cnd_000007 1
 
< 0.1%
c_cnd_000008 1
 
< 0.1%
c_cnd_000009 1
 
< 0.1%
c_cnd_000010 1
 
< 0.1%
c_cnd_000011 1
 
< 0.1%
c_cnd_000046 1
 
< 0.1%
Other values (23896) 23896
> 99.9%
2024-05-30T11:00:21.797296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 47812
16.7%
_ 47812
16.7%
0 44089
15.4%
N 23906
8.3%
D 23906
8.3%
1 20181
7.0%
2 14088
 
4.9%
3 10088
 
3.5%
5 9181
 
3.2%
4 9181
 
3.2%
Other values (4) 36628
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286872
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 47812
16.7%
_ 47812
16.7%
0 44089
15.4%
N 23906
8.3%
D 23906
8.3%
1 20181
7.0%
2 14088
 
4.9%
3 10088
 
3.5%
5 9181
 
3.2%
4 9181
 
3.2%
Other values (4) 36628
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286872
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 47812
16.7%
_ 47812
16.7%
0 44089
15.4%
N 23906
8.3%
D 23906
8.3%
1 20181
7.0%
2 14088
 
4.9%
3 10088
 
3.5%
5 9181
 
3.2%
4 9181
 
3.2%
Other values (4) 36628
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286872
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 47812
16.7%
_ 47812
16.7%
0 44089
15.4%
N 23906
8.3%
D 23906
8.3%
1 20181
7.0%
2 14088
 
4.9%
3 10088
 
3.5%
5 9181
 
3.2%
4 9181
 
3.2%
Other values (4) 36628
12.8%

Date
Date

Distinct612
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Minimum2022-01-02 00:00:00
Maximum2023-12-31 00:00:00
2024-05-30T11:00:22.153906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:22.546031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3022
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:23.334244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length16
Median length15
Mean length5.8355643
Min length1

Characters and Unicode

Total characters139505
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1169 ?
Unique (%)4.9%

Sample

1st rowGeraldine
2nd rowGia
3rd rowGianna
4th rowGiselle
5th rowGrace
ValueCountFrequency (%)
thomas 92
 
0.4%
emma 91
 
0.4%
lucas 88
 
0.4%
nathan 80
 
0.3%
louis 77
 
0.3%
lea 76
 
0.3%
chloe 74
 
0.3%
paul 71
 
0.3%
theo 65
 
0.3%
sarah 65
 
0.3%
Other values (2994) 23168
96.7%
2024-05-30T11:00:24.781184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19379
13.9%
e 14302
 
10.3%
i 12093
 
8.7%
n 11632
 
8.3%
l 9377
 
6.7%
r 7174
 
5.1%
o 6199
 
4.4%
s 4575
 
3.3%
h 4428
 
3.2%
y 4247
 
3.0%
Other values (45) 46099
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 19379
13.9%
e 14302
 
10.3%
i 12093
 
8.7%
n 11632
 
8.3%
l 9377
 
6.7%
r 7174
 
5.1%
o 6199
 
4.4%
s 4575
 
3.3%
h 4428
 
3.2%
y 4247
 
3.0%
Other values (45) 46099
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 19379
13.9%
e 14302
 
10.3%
i 12093
 
8.7%
n 11632
 
8.3%
l 9377
 
6.7%
r 7174
 
5.1%
o 6199
 
4.4%
s 4575
 
3.3%
h 4428
 
3.2%
y 4247
 
3.0%
Other values (45) 46099
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 19379
13.9%
e 14302
 
10.3%
i 12093
 
8.7%
n 11632
 
8.3%
l 9377
 
6.7%
r 7174
 
5.1%
o 6199
 
4.4%
s 4575
 
3.3%
h 4428
 
3.2%
y 4247
 
3.0%
Other values (45) 46099
33.0%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Male
18798 
Female
5108 

Length

Max length6
Median length4
Mean length4.4273404
Min length4

Characters and Unicode

Total characters105840
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 18798
78.6%
Female 5108
 
21.4%

Length

2024-05-30T11:00:25.304926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:25.790337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 18798
78.6%
female 5108
 
21.4%

Most occurring characters

ValueCountFrequency (%)
e 29014
27.4%
a 23906
22.6%
l 23906
22.6%
M 18798
17.8%
F 5108
 
4.8%
m 5108
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29014
27.4%
a 23906
22.6%
l 23906
22.6%
M 18798
17.8%
F 5108
 
4.8%
m 5108
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29014
27.4%
a 23906
22.6%
l 23906
22.6%
M 18798
17.8%
F 5108
 
4.8%
m 5108
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29014
27.4%
a 23906
22.6%
l 23906
22.6%
M 18798
17.8%
F 5108
 
4.8%
m 5108
 
4.8%

Annual_Income
Real number (ℝ)

Distinct2508
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean830840.29
Minimum10080
Maximum11200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:26.332690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10080
5-th percentile13500
Q1386000
median735000
Q31175750
95-th percentile2100000
Maximum11200000
Range11189920
Interquartile range (IQR)789750

Descriptive statistics

Standard deviation720006.4
Coefficient of variation (CV)0.86660025
Kurtosis7.5723739
Mean830840.29
Median Absolute Deviation (MAD)395000
Skewness1.7398384
Sum1.9862068 × 1010
Variance5.1840921 × 1011
MonotonicityNot monotonic
2024-05-30T11:00:27.011697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13500 5273
 
22.1%
1100000 162
 
0.7%
600000 160
 
0.7%
800000 151
 
0.6%
1300000 148
 
0.6%
1200000 142
 
0.6%
650000 141
 
0.6%
1000000 133
 
0.6%
900000 132
 
0.6%
700000 128
 
0.5%
Other values (2498) 17336
72.5%
ValueCountFrequency (%)
10080 1
 
< 0.1%
13500 5273
22.1%
24000 1
 
< 0.1%
85000 1
 
< 0.1%
106000 1
 
< 0.1%
121000 1
 
< 0.1%
131000 1
 
< 0.1%
145000 2
 
< 0.1%
160000 3
 
< 0.1%
170000 2
 
< 0.1%
ValueCountFrequency (%)
11200000 1
< 0.1%
8000000 1
< 0.1%
7650000 1
< 0.1%
6800000 1
< 0.1%
6600000 1
< 0.1%
6500000 1
< 0.1%
6460000 1
< 0.1%
6400000 2
< 0.1%
6240000 1
< 0.1%
6125000 1
< 0.1%

Dealer_Name
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Progressive Shippers Cooperative Association No
 
1318
Rabun Used Car Sales
 
1313
Race Car Help
 
1253
Saab-Belle Dodge
 
1251
Star Enterprises Inc
 
1249
Other values (23)
17522 

Length

Max length47
Median length30
Mean length21.805614
Min length9

Characters and Unicode

Total characters521285
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuddy Storbeck's Diesel Service Inc
2nd rowC & M Motors Inc
3rd rowCapitol KIA
4th rowChrysler of Tri-Cities
5th rowChrysler Plymouth

Common Values

ValueCountFrequency (%)
Progressive Shippers Cooperative Association No 1318
 
5.5%
Rabun Used Car Sales 1313
 
5.5%
Race Car Help 1253
 
5.2%
Saab-Belle Dodge 1251
 
5.2%
Star Enterprises Inc 1249
 
5.2%
Tri-State Mack Inc 1249
 
5.2%
Ryder Truck Rental and Leasing 1248
 
5.2%
U-Haul CO 1247
 
5.2%
Scrivener Performance Engineering 1246
 
5.2%
Suburban Ford 1243
 
5.2%
Other values (18) 11289
47.2%

Length

2024-05-30T11:00:27.742303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 4374
 
5.6%
car 3191
 
4.0%
sales 2570
 
3.3%
dodge 1880
 
2.4%
chrysler 1880
 
2.4%
ford 1872
 
2.4%
co 1871
 
2.4%
association 1318
 
1.7%
cooperative 1318
 
1.7%
shippers 1318
 
1.7%
Other values (67) 57214
72.6%

Most occurring characters

ValueCountFrequency (%)
54900
 
10.5%
e 53665
 
10.3%
r 39723
 
7.6%
a 34108
 
6.5%
n 28257
 
5.4%
o 26081
 
5.0%
i 25985
 
5.0%
s 24245
 
4.7%
t 21426
 
4.1%
c 21343
 
4.1%
Other values (37) 191552
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 521285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
54900
 
10.5%
e 53665
 
10.3%
r 39723
 
7.6%
a 34108
 
6.5%
n 28257
 
5.4%
o 26081
 
5.0%
i 25985
 
5.0%
s 24245
 
4.7%
t 21426
 
4.1%
c 21343
 
4.1%
Other values (37) 191552
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 521285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
54900
 
10.5%
e 53665
 
10.3%
r 39723
 
7.6%
a 34108
 
6.5%
n 28257
 
5.4%
o 26081
 
5.0%
i 25985
 
5.0%
s 24245
 
4.7%
t 21426
 
4.1%
c 21343
 
4.1%
Other values (37) 191552
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 521285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
54900
 
10.5%
e 53665
 
10.3%
r 39723
 
7.6%
a 34108
 
6.5%
n 28257
 
5.4%
o 26081
 
5.0%
i 25985
 
5.0%
s 24245
 
4.7%
t 21426
 
4.1%
c 21343
 
4.1%
Other values (37) 191552
36.7%

Company
Categorical

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Chevrolet
1819 
Dodge
1671 
Ford
 
1614
Volkswagen
 
1333
Mercedes-B
 
1285
Other values (25)
16184 

Length

Max length10
Median length8
Mean length6.8641345
Min length3

Characters and Unicode

Total characters164094
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFord
2nd rowDodge
3rd rowCadillac
4th rowToyota
5th rowAcura

Common Values

ValueCountFrequency (%)
Chevrolet 1819
 
7.6%
Dodge 1671
 
7.0%
Ford 1614
 
6.8%
Volkswagen 1333
 
5.6%
Mercedes-B 1285
 
5.4%
Mitsubishi 1277
 
5.3%
Chrysler 1120
 
4.7%
Oldsmobile 1111
 
4.6%
Toyota 1110
 
4.6%
Nissan 886
 
3.7%
Other values (20) 10680
44.7%

Length

2024-05-30T11:00:28.414787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chevrolet 1819
 
7.6%
dodge 1671
 
7.0%
ford 1614
 
6.8%
volkswagen 1333
 
5.6%
mercedes-b 1285
 
5.4%
mitsubishi 1277
 
5.3%
chrysler 1120
 
4.7%
oldsmobile 1111
 
4.6%
toyota 1110
 
4.6%
nissan 886
 
3.7%
Other values (20) 10680
44.7%

Most occurring characters

ValueCountFrequency (%)
e 15491
 
9.4%
o 14320
 
8.7%
r 10927
 
6.7%
s 10338
 
6.3%
l 9696
 
5.9%
i 9524
 
5.8%
a 8861
 
5.4%
d 7773
 
4.7%
u 7006
 
4.3%
t 6400
 
3.9%
Other values (31) 63758
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 164094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15491
 
9.4%
o 14320
 
8.7%
r 10927
 
6.7%
s 10338
 
6.3%
l 9696
 
5.9%
i 9524
 
5.8%
a 8861
 
5.4%
d 7773
 
4.7%
u 7006
 
4.3%
t 6400
 
3.9%
Other values (31) 63758
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 164094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15491
 
9.4%
o 14320
 
8.7%
r 10927
 
6.7%
s 10338
 
6.3%
l 9696
 
5.9%
i 9524
 
5.8%
a 8861
 
5.4%
d 7773
 
4.7%
u 7006
 
4.3%
t 6400
 
3.9%
Other values (31) 63758
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 164094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15491
 
9.4%
o 14320
 
8.7%
r 10927
 
6.7%
s 10338
 
6.3%
l 9696
 
5.9%
i 9524
 
5.8%
a 8861
 
5.4%
d 7773
 
4.7%
u 7006
 
4.3%
t 6400
 
3.9%
Other values (31) 63758
38.9%

Model
Text

Distinct154
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:29.452409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.5813185
Min length2

Characters and Unicode

Total characters157333
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExpedition
2nd rowDurango
3rd rowEldorado
4th rowCelica
5th rowTL
ValueCountFrequency (%)
grand 700
 
2.6%
ram 658
 
2.4%
coupe 435
 
1.6%
diamante 418
 
1.6%
silhouette 411
 
1.5%
prizm 411
 
1.5%
passat 391
 
1.5%
pickup 383
 
1.4%
jetta 382
 
1.4%
rl 372
 
1.4%
Other values (151) 22385
83.1%
2024-05-30T11:00:30.965492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14726
 
9.4%
r 13076
 
8.3%
e 12819
 
8.1%
o 9082
 
5.8%
t 8224
 
5.2%
i 8111
 
5.2%
n 7890
 
5.0%
C 5519
 
3.5%
l 5480
 
3.5%
S 5115
 
3.3%
Other values (50) 67291
42.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14726
 
9.4%
r 13076
 
8.3%
e 12819
 
8.1%
o 9082
 
5.8%
t 8224
 
5.2%
i 8111
 
5.2%
n 7890
 
5.0%
C 5519
 
3.5%
l 5480
 
3.5%
S 5115
 
3.3%
Other values (50) 67291
42.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14726
 
9.4%
r 13076
 
8.3%
e 12819
 
8.1%
o 9082
 
5.8%
t 8224
 
5.2%
i 8111
 
5.2%
n 7890
 
5.0%
C 5519
 
3.5%
l 5480
 
3.5%
S 5115
 
3.3%
Other values (50) 67291
42.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14726
 
9.4%
r 13076
 
8.3%
e 12819
 
8.1%
o 9082
 
5.8%
t 8224
 
5.2%
i 8111
 
5.2%
n 7890
 
5.0%
C 5519
 
3.5%
l 5480
 
3.5%
S 5115
 
3.3%
Other values (50) 67291
42.8%

Engine
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Double Overhead Camshaft
12571 
Overhead Camshaft
11335 

Length

Max length27
Median length27
Mean length22.258513
Min length17

Characters and Unicode

Total characters532112
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDouble Overhead Camshaft
2nd rowDouble Overhead Camshaft
3rd rowOverhead Camshaft
4th rowOverhead Camshaft
5th rowDouble Overhead Camshaft

Common Values

ValueCountFrequency (%)
Double Overhead Camshaft 12571
52.6%
Overhead Camshaft 11335
47.4%

Length

2024-05-30T11:00:31.565485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:31.968233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
overhead 23906
39.6%
camshaft 23906
39.6%
doubleã‚â 12571
20.8%

Most occurring characters

ValueCountFrequency (%)
a 71718
 
13.5%
e 60383
 
11.3%
h 47812
 
9.0%
v 23906
 
4.5%
r 23906
 
4.5%
f 23906
 
4.5%
s 23906
 
4.5%
m 23906
 
4.5%
C 23906
 
4.5%
23906
 
4.5%
Other values (12) 184857
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 71718
 
13.5%
e 60383
 
11.3%
h 47812
 
9.0%
v 23906
 
4.5%
r 23906
 
4.5%
f 23906
 
4.5%
s 23906
 
4.5%
m 23906
 
4.5%
C 23906
 
4.5%
23906
 
4.5%
Other values (12) 184857
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 71718
 
13.5%
e 60383
 
11.3%
h 47812
 
9.0%
v 23906
 
4.5%
r 23906
 
4.5%
f 23906
 
4.5%
s 23906
 
4.5%
m 23906
 
4.5%
C 23906
 
4.5%
23906
 
4.5%
Other values (12) 184857
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 71718
 
13.5%
e 60383
 
11.3%
h 47812
 
9.0%
v 23906
 
4.5%
r 23906
 
4.5%
f 23906
 
4.5%
s 23906
 
4.5%
m 23906
 
4.5%
C 23906
 
4.5%
23906
 
4.5%
Other values (12) 184857
34.7%

Transmission
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Auto
12571 
Manual
11335 

Length

Max length6
Median length4
Mean length4.9482975
Min length4

Characters and Unicode

Total characters118294
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAuto
2nd rowAuto
3rd rowManual
4th rowManual
5th rowAuto

Common Values

ValueCountFrequency (%)
Auto 12571
52.6%
Manual 11335
47.4%

Length

2024-05-30T11:00:32.376361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:32.666640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
auto 12571
52.6%
manual 11335
47.4%

Most occurring characters

ValueCountFrequency (%)
u 23906
20.2%
a 22670
19.2%
A 12571
10.6%
t 12571
10.6%
o 12571
10.6%
M 11335
9.6%
n 11335
9.6%
l 11335
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 118294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 23906
20.2%
a 22670
19.2%
A 12571
10.6%
t 12571
10.6%
o 12571
10.6%
M 11335
9.6%
n 11335
9.6%
l 11335
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 118294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 23906
20.2%
a 22670
19.2%
A 12571
10.6%
t 12571
10.6%
o 12571
10.6%
M 11335
9.6%
n 11335
9.6%
l 11335
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 118294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 23906
20.2%
a 22670
19.2%
A 12571
10.6%
t 12571
10.6%
o 12571
10.6%
M 11335
9.6%
n 11335
9.6%
l 11335
9.6%

Color
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Pale White
11256 
Black
7857 
Red
4793 

Length

Max length10
Median length5
Mean length6.9532335
Min length3

Characters and Unicode

Total characters166224
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlack
2nd rowBlack
3rd rowRed
4th rowPale White
5th rowRed

Common Values

ValueCountFrequency (%)
Pale White 11256
47.1%
Black 7857
32.9%
Red 4793
20.0%

Length

2024-05-30T11:00:32.929907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:33.186903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pale 11256
32.0%
white 11256
32.0%
black 7857
22.3%
red 4793
13.6%

Most occurring characters

ValueCountFrequency (%)
e 27305
16.4%
a 19113
11.5%
l 19113
11.5%
P 11256
6.8%
11256
6.8%
W 11256
6.8%
h 11256
6.8%
i 11256
6.8%
t 11256
6.8%
B 7857
 
4.7%
Other values (4) 25300
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 27305
16.4%
a 19113
11.5%
l 19113
11.5%
P 11256
6.8%
11256
6.8%
W 11256
6.8%
h 11256
6.8%
i 11256
6.8%
t 11256
6.8%
B 7857
 
4.7%
Other values (4) 25300
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 27305
16.4%
a 19113
11.5%
l 19113
11.5%
P 11256
6.8%
11256
6.8%
W 11256
6.8%
h 11256
6.8%
i 11256
6.8%
t 11256
6.8%
B 7857
 
4.7%
Other values (4) 25300
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 27305
16.4%
a 19113
11.5%
l 19113
11.5%
P 11256
6.8%
11256
6.8%
W 11256
6.8%
h 11256
6.8%
i 11256
6.8%
t 11256
6.8%
B 7857
 
4.7%
Other values (4) 25300
15.2%

Price_($)
Real number (ℝ)

HIGH CORRELATION 

Distinct870
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28090.248
Minimum1200
Maximum85800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:33.494926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile12000
Q118001
median23000
Q334000
95-th percentile61000
Maximum85800
Range84600
Interquartile range (IQR)15999

Descriptive statistics

Standard deviation14788.688
Coefficient of variation (CV)0.52647053
Kurtosis2.0083154
Mean28090.248
Median Absolute Deviation (MAD)6300
Skewness1.4663159
Sum6.7152546 × 108
Variance2.1870528 × 108
MonotonicityNot monotonic
2024-05-30T11:00:33.829575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22000 1191
 
5.0%
19000 974
 
4.1%
21000 873
 
3.7%
26000 689
 
2.9%
18000 627
 
2.6%
17000 621
 
2.6%
16000 620
 
2.6%
12000 619
 
2.6%
31000 604
 
2.5%
20000 533
 
2.2%
Other values (860) 16555
69.3%
ValueCountFrequency (%)
1200 1
 
< 0.1%
1450 1
 
< 0.1%
1700 1
 
< 0.1%
2200 1
 
< 0.1%
4200 1
 
< 0.1%
4300 1
 
< 0.1%
9000 169
0.7%
9001 59
 
0.2%
9100 3
 
< 0.1%
9200 3
 
< 0.1%
ValueCountFrequency (%)
85800 1
 
< 0.1%
85601 1
 
< 0.1%
85600 2
 
< 0.1%
85500 2
 
< 0.1%
85400 1
 
< 0.1%
85301 1
 
< 0.1%
85300 1
 
< 0.1%
85250 1
 
< 0.1%
85200 1
 
< 0.1%
85001 22
0.1%

Dealer_No
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
85257-3102
3814 
53546-9427
3813 
78758-7841
3753 
06457-3834
3132 
38701-8047
3132 
Other values (2)
6262 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters239060
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row06457-3834
2nd row60504-7114
3rd row38701-8047
4th row99301-3882
5th row53546-9427

Common Values

ValueCountFrequency (%)
85257-3102 3814
16.0%
53546-9427 3813
15.9%
78758-7841 3753
15.7%
06457-3834 3132
13.1%
38701-8047 3132
13.1%
99301-3882 3132
13.1%
60504-7114 3130
13.1%

Length

2024-05-30T11:00:34.142858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:34.403095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
85257-3102 3814
16.0%
53546-9427 3813
15.9%
78758-7841 3753
15.7%
06457-3834 3132
13.1%
38701-8047 3132
13.1%
99301-3882 3132
13.1%
60504-7114 3130
13.1%

Most occurring characters

ValueCountFrequency (%)
7 31412
13.1%
8 30733
12.9%
4 27035
11.3%
5 25269
10.6%
- 23906
10.0%
3 23287
9.7%
0 22602
9.5%
1 20091
8.4%
2 14573
6.1%
9 10077
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 31412
13.1%
8 30733
12.9%
4 27035
11.3%
5 25269
10.6%
- 23906
10.0%
3 23287
9.7%
0 22602
9.5%
1 20091
8.4%
2 14573
6.1%
9 10077
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 31412
13.1%
8 30733
12.9%
4 27035
11.3%
5 25269
10.6%
- 23906
10.0%
3 23287
9.7%
0 22602
9.5%
1 20091
8.4%
2 14573
6.1%
9 10077
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 31412
13.1%
8 30733
12.9%
4 27035
11.3%
5 25269
10.6%
- 23906
10.0%
3 23287
9.7%
0 22602
9.5%
1 20091
8.4%
2 14573
6.1%
9 10077
 
4.2%

Body_Style
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
SUV
6374 
Hatchback
6128 
Sedan
4488 
Passenger
3945 
Hardtop
2971 

Length

Max length9
Median length7
Mean length6.4007362
Min length3

Characters and Unicode

Total characters153016
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUV
2nd rowSUV
3rd rowPassenger
4th rowSUV
5th rowHatchback

Common Values

ValueCountFrequency (%)
SUV 6374
26.7%
Hatchback 6128
25.6%
Sedan 4488
18.8%
Passenger 3945
16.5%
Hardtop 2971
12.4%

Length

2024-05-30T11:00:34.758708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:35.027307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
suv 6374
26.7%
hatchback 6128
25.6%
sedan 4488
18.8%
passenger 3945
16.5%
hardtop 2971
12.4%

Most occurring characters

ValueCountFrequency (%)
a 23660
15.5%
e 12378
 
8.1%
c 12256
 
8.0%
S 10862
 
7.1%
H 9099
 
5.9%
t 9099
 
5.9%
n 8433
 
5.5%
s 7890
 
5.2%
d 7459
 
4.9%
r 6916
 
4.5%
Other values (9) 44964
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 153016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 23660
15.5%
e 12378
 
8.1%
c 12256
 
8.0%
S 10862
 
7.1%
H 9099
 
5.9%
t 9099
 
5.9%
n 8433
 
5.5%
s 7890
 
5.2%
d 7459
 
4.9%
r 6916
 
4.5%
Other values (9) 44964
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 153016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 23660
15.5%
e 12378
 
8.1%
c 12256
 
8.0%
S 10862
 
7.1%
H 9099
 
5.9%
t 9099
 
5.9%
n 8433
 
5.5%
s 7890
 
5.2%
d 7459
 
4.9%
r 6916
 
4.5%
Other values (9) 44964
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 153016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 23660
15.5%
e 12378
 
8.1%
c 12256
 
8.0%
S 10862
 
7.1%
H 9099
 
5.9%
t 9099
 
5.9%
n 8433
 
5.5%
s 7890
 
5.2%
d 7459
 
4.9%
r 6916
 
4.5%
Other values (9) 44964
29.4%

Phone
Real number (ℝ)

Distinct23804
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7497740.6
Minimum6000101
Maximum8999579
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:35.343985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6000101
5-th percentile6146284
Q16746495
median7496197.5
Q38248146.2
95-th percentile8849134.5
Maximum8999579
Range2999478
Interquartile range (IQR)1501651.2

Descriptive statistics

Standard deviation867492
Coefficient of variation (CV)0.11570045
Kurtosis-1.1979186
Mean7497740.6
Median Absolute Deviation (MAD)750964
Skewness0.00086348034
Sum1.7924099 × 1011
Variance7.5254236 × 1011
MonotonicityNot monotonic
2024-05-30T11:00:35.710753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6005535 2
 
< 0.1%
7298842 2
 
< 0.1%
6955907 2
 
< 0.1%
6854031 2
 
< 0.1%
8895808 2
 
< 0.1%
8389695 2
 
< 0.1%
7390875 2
 
< 0.1%
8168355 2
 
< 0.1%
8602618 2
 
< 0.1%
6257129 2
 
< 0.1%
Other values (23794) 23886
99.9%
ValueCountFrequency (%)
6000101 1
< 0.1%
6000191 1
< 0.1%
6000247 1
< 0.1%
6000326 1
< 0.1%
6000356 1
< 0.1%
6000458 1
< 0.1%
6000493 1
< 0.1%
6000559 1
< 0.1%
6000740 1
< 0.1%
6000749 1
< 0.1%
ValueCountFrequency (%)
8999579 1
< 0.1%
8999305 1
< 0.1%
8998913 1
< 0.1%
8998867 1
< 0.1%
8998864 1
< 0.1%
8998836 1
< 0.1%
8998761 1
< 0.1%
8998696 1
< 0.1%
8998568 1
< 0.1%
8998480 1
< 0.1%

Amount_paid_for_insurance
Real number (ℝ)

HIGH CORRELATION 

Distinct3421
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1654.5178
Minimum103
Maximum4762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:36.067754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile808
Q11138
median1432
Q31990
95-th percentile3291
Maximum4762
Range4659
Interquartile range (IQR)852

Descriptive statistics

Standard deviation753.48166
Coefficient of variation (CV)0.45540862
Kurtosis1.8423571
Mean1654.5178
Median Absolute Deviation (MAD)357
Skewness1.3806533
Sum39552902
Variance567734.62
MonotonicityNot monotonic
2024-05-30T11:00:36.393381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1369 32
 
0.1%
1163 32
 
0.1%
1307 31
 
0.1%
1311 31
 
0.1%
1275 30
 
0.1%
1325 30
 
0.1%
1408 30
 
0.1%
1305 30
 
0.1%
1069 29
 
0.1%
1321 29
 
0.1%
Other values (3411) 23602
98.7%
ValueCountFrequency (%)
103 1
< 0.1%
187 1
< 0.1%
450 1
< 0.1%
453 1
< 0.1%
460 1
< 0.1%
463 1
< 0.1%
464 2
< 0.1%
465 1
< 0.1%
470 1
< 0.1%
471 2
< 0.1%
ValueCountFrequency (%)
4762 1
< 0.1%
4753 1
< 0.1%
4749 1
< 0.1%
4748 1
< 0.1%
4738 1
< 0.1%
4737 1
< 0.1%
4733 1
< 0.1%
4726 1
< 0.1%
4724 1
< 0.1%
4698 1
< 0.1%

Claim_amount
Real number (ℝ)

ZEROS 

Distinct230
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.46327
Minimum0
Maximum8560
Zeros21516
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size186.9 KiB
2024-05-30T11:00:37.139333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2200
Maximum8560
Range8560
Interquartile range (IQR)0

Descriptive statistics

Standard deviation977.50653
Coefficient of variation (CV)3.46065
Kurtosis20.505985
Mean282.46327
Median Absolute Deviation (MAD)0
Skewness4.2414879
Sum6752567
Variance955519.01
MonotonicityNot monotonic
2024-05-30T11:00:37.466985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21516
90.0%
2200 150
 
0.6%
1900 138
 
0.6%
2100 112
 
0.5%
1600 95
 
0.4%
1700 94
 
0.4%
1200 92
 
0.4%
2600 80
 
0.3%
3100 77
 
0.3%
1800 77
 
0.3%
Other values (220) 1475
 
6.2%
ValueCountFrequency (%)
0 21516
90.0%
145 1
 
< 0.1%
900 19
 
0.1%
925 1
 
< 0.1%
950 3
 
< 0.1%
960 1
 
< 0.1%
980 1
 
< 0.1%
1000 17
 
0.1%
1040 1
 
< 0.1%
1050 1
 
< 0.1%
ValueCountFrequency (%)
8560 3
 
< 0.1%
8540 1
 
< 0.1%
8530 1
 
< 0.1%
8500 6
 
< 0.1%
8250 1
 
< 0.1%
8200 11
< 0.1%
7560 1
 
< 0.1%
7550 1
 
< 0.1%
7510 1
 
< 0.1%
7500 15
0.1%

City
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
Liepaja
4057 
Tukums
4008 
Riga
3987 
Daugavpils
3976 
Jelgava
3960 

Length

Max length10
Median length9
Mean length7.1587468
Min length4

Characters and Unicode

Total characters171137
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRiga
2nd rowLiepaja
3rd rowRiga
4th rowJelgava
5th rowLiepaja

Common Values

ValueCountFrequency (%)
Liepaja 4057
17.0%
Tukums 4008
16.8%
Riga 3987
16.7%
Daugavpils 3976
16.6%
Jelgava 3960
16.6%
Ventspils 3918
16.4%

Length

2024-05-30T11:00:37.803153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:38.077358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
liepaja 4057
17.0%
tukums 4008
16.8%
riga 3987
16.7%
daugavpils 3976
16.6%
jelgava 3960
16.6%
ventspils 3918
16.4%

Most occurring characters

ValueCountFrequency (%)
a 27973
16.3%
i 15938
9.3%
s 15820
 
9.2%
u 11992
 
7.0%
p 11951
 
7.0%
e 11935
 
7.0%
g 11923
 
7.0%
l 11854
 
6.9%
v 7936
 
4.6%
L 4057
 
2.4%
Other values (10) 39758
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27973
16.3%
i 15938
9.3%
s 15820
 
9.2%
u 11992
 
7.0%
p 11951
 
7.0%
e 11935
 
7.0%
g 11923
 
7.0%
l 11854
 
6.9%
v 7936
 
4.6%
L 4057
 
2.4%
Other values (10) 39758
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27973
16.3%
i 15938
9.3%
s 15820
 
9.2%
u 11992
 
7.0%
p 11951
 
7.0%
e 11935
 
7.0%
g 11923
 
7.0%
l 11854
 
6.9%
v 7936
 
4.6%
L 4057
 
2.4%
Other values (10) 39758
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27973
16.3%
i 15938
9.3%
s 15820
 
9.2%
u 11992
 
7.0%
p 11951
 
7.0%
e 11935
 
7.0%
g 11923
 
7.0%
l 11854
 
6.9%
v 7936
 
4.6%
L 4057
 
2.4%
Other values (10) 39758
23.2%

Year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.9 KiB
2023
13261 
2022
10645 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters95624
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2023 13261
55.5%
2022 10645
44.5%

Length

2024-05-30T11:00:38.389025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-30T11:00:38.610562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2023 13261
55.5%
2022 10645
44.5%

Most occurring characters

ValueCountFrequency (%)
2 58457
61.1%
0 23906
25.0%
3 13261
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 58457
61.1%
0 23906
25.0%
3 13261
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 58457
61.1%
0 23906
25.0%
3 13261
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 58457
61.1%
0 23906
25.0%
3 13261
 
13.9%

Month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.852924
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.5 KiB
2024-05-30T11:00:38.835734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2579854
Coefficient of variation (CV)0.41487546
Kurtosis-0.99435964
Mean7.852924
Median Absolute Deviation (MAD)2
Skewness-0.41558155
Sum187732
Variance10.614469
MonotonicityNot monotonic
2024-05-30T11:00:39.114517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 3546
14.8%
11 3470
14.5%
9 3305
13.8%
5 1895
7.9%
10 1830
7.7%
7 1725
7.2%
6 1715
7.2%
8 1705
7.1%
4 1655
6.9%
3 1535
6.4%
Other values (2) 1525
6.4%
ValueCountFrequency (%)
1 790
 
3.3%
2 735
 
3.1%
3 1535
6.4%
4 1655
6.9%
5 1895
7.9%
6 1715
7.2%
7 1725
7.2%
8 1705
7.1%
9 3305
13.8%
10 1830
7.7%
ValueCountFrequency (%)
12 3546
14.8%
11 3470
14.5%
10 1830
7.7%
9 3305
13.8%
8 1705
7.1%
7 1725
7.2%
6 1715
7.2%
5 1895
7.9%
4 1655
6.9%
3 1535
6.4%

Day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.473061
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size93.5 KiB
2024-05-30T11:00:39.392807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7341721
Coefficient of variation (CV)0.56447603
Kurtosis-1.1906513
Mean15.473061
Median Absolute Deviation (MAD)7
Skewness0.036949301
Sum369899
Variance76.285762
MonotonicityNot monotonic
2024-05-30T11:00:39.733983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 1005
 
4.2%
8 975
 
4.1%
12 940
 
3.9%
26 930
 
3.9%
21 905
 
3.8%
25 880
 
3.7%
13 880
 
3.7%
17 840
 
3.5%
3 835
 
3.5%
10 805
 
3.4%
Other values (21) 14911
62.4%
ValueCountFrequency (%)
1 795
3.3%
2 755
3.2%
3 835
3.5%
4 765
3.2%
5 1005
4.2%
6 705
2.9%
7 675
2.8%
8 975
4.1%
9 725
3.0%
10 805
3.4%
ValueCountFrequency (%)
31 495
2.1%
30 615
2.6%
29 551
2.3%
28 705
2.9%
27 670
2.8%
26 930
3.9%
25 880
3.7%
24 760
3.2%
23 800
3.3%
22 725
3.0%

Interactions

2024-05-30T11:00:15.312665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:50.500238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:56.138006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:59.474689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:02.761467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:06.220411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:11.866854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:15.748520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:51.257189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:56.556068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:59.793642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:03.484281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:06.881075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:12.685991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:16.185298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:52.221314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:56.896147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:00.189239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:03.945308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:07.779622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:13.137752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:16.609606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:53.230909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:57.702107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:00.832955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:04.452631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:08.613736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:13.613677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:17.039250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:54.030051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:58.040153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:01.338436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:04.906023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:09.486591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:14.032506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:17.541471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:54.669568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:58.429286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:01.743912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:05.373115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:10.222148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:14.417250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:18.142241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:55.490024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T10:59:58.936341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:02.133962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:05.737410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:10.899841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-30T11:00:14.932352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-30T11:00:40.061689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Amount_paid_for_insuranceAnnual_IncomeBody_StyleCityClaim_amountColorCompanyDayDealer_NameDealer_NoEngineGenderMonthPhonePrice_($)TransmissionYear
Amount_paid_for_insurance1.0000.0120.0840.0000.0140.0930.158-0.0020.0000.0000.1350.0000.0050.0000.9520.1350.000
Annual_Income0.0121.0000.0060.011-0.0010.0000.0110.0080.0000.0000.0000.063-0.008-0.0020.0170.0000.041
Body_Style0.0840.0061.0000.000-0.0010.0360.3160.0020.0000.0000.0830.000-0.016-0.0100.0140.0830.065
City0.0000.0110.0001.0000.0030.0000.000-0.0000.0110.0090.0000.0000.021-0.0100.0040.0000.018
Claim_amount0.014-0.001-0.0010.0031.0000.0280.0680.0040.0040.0000.0700.0000.001-0.0040.0140.0700.000
Color0.0930.0000.0360.0000.0281.0000.1830.0070.0000.0000.0000.000-0.0300.002-0.0060.0000.030
Company0.1580.0110.3160.0000.0680.1831.0000.0100.0000.0000.2500.018-0.0020.0020.0240.2500.000
Day-0.0020.0080.002-0.0000.0040.0070.0101.0000.0000.0000.0140.000-0.015-0.005-0.0020.0140.043
Dealer_Name0.0000.0000.0000.0110.0040.0000.0000.0001.0001.0000.0000.010-0.000-0.0040.0070.0000.000
Dealer_No0.0000.0000.0000.0090.0000.0000.0000.0001.0001.0000.0000.0000.001-0.0110.0040.0000.000
Engine0.1350.0000.0830.0000.0700.0000.2500.0140.0000.0001.0000.000-0.008-0.005-0.0581.0000.007
Gender0.0000.0630.0000.0000.0000.0000.0180.0000.0100.0000.0001.0000.035-0.002-0.0030.0000.028
Month0.005-0.008-0.0160.0210.001-0.030-0.002-0.015-0.0000.001-0.0080.0351.0000.0050.0060.0060.052
Phone0.000-0.002-0.010-0.010-0.0040.0020.002-0.005-0.004-0.011-0.005-0.0020.0051.000-0.0000.0000.000
Price_($)0.9520.0170.0140.0040.014-0.0060.024-0.0020.0070.004-0.058-0.0030.006-0.0001.0000.2090.000
Transmission0.1350.0000.0830.0000.0700.0000.2500.0140.0000.0001.0000.0000.0060.0000.2091.0000.007
Year0.0000.0410.0650.0180.0000.0300.0000.0430.0000.0000.0070.0280.0520.0000.0000.0071.000

Missing values

2024-05-30T11:00:19.428334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-30T11:00:20.300889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Car_idDateCustomer_NameGenderAnnual_IncomeDealer_NameCompanyModelEngineTransmissionColorPrice_($)Dealer_NoBody_StylePhoneAmount_paid_for_insuranceClaim_amountCityYearMonthDay
0C_CND_0000012022-01-02GeraldineMale13500Buddy Storbeck's Diesel Service IncFordExpeditionDouble Overhead CamshaftAutoBlack2600006457-3834SUV826467816650.00Riga202212
1C_CND_0000022022-01-02GiaMale1480000C & M Motors IncDodgeDurangoDouble Overhead CamshaftAutoBlack1900060504-7114SUV684818913321900.00Liepaja202212
2C_CND_0000032022-01-02GiannaMale1035000Capitol KIACadillacEldoradoOverhead CamshaftManualRed3150038701-8047Passenger729879818970.00Riga202212
3C_CND_0000042022-01-02GiselleMale13500Chrysler of Tri-CitiesToyotaCelicaOverhead CamshaftManualPale White1400099301-3882SUV625755711760.00Jelgava202212
4C_CND_0000052022-01-02GraceMale1465000Chrysler PlymouthAcuraTLDouble Overhead CamshaftAutoRed2450053546-9427Hatchback708148313232450.00Liepaja202212
5C_CND_0000062022-01-02GuadalupeMale850000Classic ChevyMitsubishiDiamanteOverhead CamshaftManualPale White1200085257-3102Hatchback73152168300.00Riga202212
6C_CND_0000072022-01-02HaileyMale1600000Clay Johnson Auto SalesToyotaCorollaOverhead CamshaftManualPale White1400078758-7841Passenger77278797170.00Riga202212
7C_CND_0000082022-01-02GrahamMale13500U-Haul COMitsubishiGalantDouble Overhead CamshaftAutoPale White4200078758-7841Passenger620651221830.00Jelgava202212
8C_CND_0000092022-01-02NaomiMale815000Rabun Used Car SalesChevroletMalibuOverhead CamshaftManualPale White8200085257-3102Hardtop719485742060.00Ventspils202212
9C_CND_0000102022-01-02GraysonFemale13500Rabun Used Car SalesFordEscortDouble Overhead CamshaftAutoPale White1500085257-3102Passenger78368928730.00Ventspils202212
Car_idDateCustomer_NameGenderAnnual_IncomeDealer_NameCompanyModelEngineTransmissionColorPrice_($)Dealer_NoBody_StylePhoneAmount_paid_for_insuranceClaim_amountCityYearMonthDay
23896C_CND_0238972023-12-31SimiMale761000Rabun Used Car SalesDodgeViperDouble Overhead CamshaftAutoRed4100085257-3102SUV874424922140.00Daugavpils20231231
23897C_CND_0238982023-12-31SimoneMale520000Progressive Shippers Cooperative Association NoMercedes-BE-ClassDouble Overhead CamshaftAutoRed1500053546-9427Sedan681942211780.00Ventspils20231231
23898C_CND_0238992023-12-31SkylarMale530000Rabun Used Car SalesVolvoC70Overhead CamshaftManualPale White2400085257-3102Hatchback622518316492400.00Liepaja20231231
23899C_CND_0239002023-12-31YunaMale13500U-Haul COBuickPark AvenueDouble Overhead CamshaftAutoPale White6200078758-7841Hatchback838478533090.00Ventspils20231231
23900C_CND_0239012023-12-31NathanFemale771000Buddy Storbeck's Diesel Service IncFordContourDouble Overhead CamshaftAutoRed1900006457-3834Sedan817000310360.00Jelgava20231231
23901C_CND_0239022023-12-31MartinMale13500C & M Motors IncPlymouthVoyagerOverhead CamshaftManualRed1200060504-7114Passenger85835986921200.00Tukums20231231
23902C_CND_0239032023-12-31JimmyFemale900000Ryder Truck Rental and LeasingChevroletPrizmDouble Overhead CamshaftAutoBlack1600006457-3834Hardtop791422910720.00Liepaja20231231
23903C_CND_0239042023-12-31EmmaMale705000Chrysler of Tri-CitiesBMW328iOverhead CamshaftManualRed2100099301-3882Sedan765912710610.00Liepaja20231231
23904C_CND_0239052023-12-31VictoireMale13500Chrysler PlymouthChevroletMetroDouble Overhead CamshaftAutoBlack3100053546-9427Passenger603076419223100.00Tukums20231231
23905C_CND_0239062023-12-31DonovanMale1225000Pars Auto SalesLexusES300Double Overhead CamshaftAutoPale White2750038701-8047Hardtop702056414920.00Tukums20231231